






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DetrImageProcessor



class 				SCREAMING_SNAKE_CASE__		(							unittest.TestCase		):





					"""simple docstring"""






					def __init__(   self	:    str				,						__A	:    List[Any]				,						__A	:    Any=7				,						__A	:    Optional[Any]=3				,						__A	:    Any=3_0				,						__A	:    Optional[Any]=4_0_0				,						__A	:    Union[str, Any]=True				,						__A	:    int=None				,						__A	:    Union[str, Any]=True				,						__A	:    Tuple=1 / 2_5_5				,						__A	:    str=True				,						__A	:    Tuple=[0.5, 0.5, 0.5]				,						__A	:    Optional[int]=[0.5, 0.5, 0.5]				,						__A	:    Tuple=True				,						):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__					: int  =    size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__					: Union[str, Any]  =    parent
						snake_case__					: Optional[Any]  =    batch_size
						snake_case__					: Tuple  =    num_channels
						snake_case__					: Any  =    min_resolution
						snake_case__					: int  =    max_resolution
						snake_case__					: str  =    do_resize
						snake_case__					: Optional[int]  =    size
						snake_case__					: List[Any]  =    do_rescale
						snake_case__					: Optional[int]  =    rescale_factor
						snake_case__					: Optional[Any]  =    do_normalize
						snake_case__					: str  =    image_mean
						snake_case__					: Union[str, Any]  =    image_std
						snake_case__					: int  =    do_pad



					def 			_lowercase	(   self	:    List[str]			):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_pad": self.do_pad,
						}






					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    Tuple				,						__A	:    int=False			):
						if not batched:
							snake_case__					: Optional[int]  =    image_inputs[0]
							if isinstance(snake_case__				,						Image.Image			):
								snake_case__,		snake_case__					: str  =    image.size
							else:
								snake_case__,		snake_case__					: str  =    image.shape[1], image.shape[2]
							if w < h:
								snake_case__					: Optional[Any]  =    int(self.size["shortest_edge"] * h / w			)
								snake_case__					: Tuple  =    self.size["shortest_edge"]
							elif w > h:
								snake_case__					: Optional[Any]  =    self.size["shortest_edge"]
								snake_case__					: List[str]  =    int(self.size["shortest_edge"] * w / h			)
							else:
								snake_case__					: Optional[int]  =    self.size["shortest_edge"]
								snake_case__					: List[Any]  =    self.size["shortest_edge"]

						else:
							snake_case__					: Dict  =    []
							for image in image_inputs:
								snake_case__,		snake_case__					: Dict  =    self.get_expected_values([image]			)
								expected_values.append((expected_height, expected_width)			)
							snake_case__					: Tuple  =    max(snake_case__				,						key=lambda __A			: item[0]			)[0]
							snake_case__					: str  =    max(snake_case__				,						key=lambda __A			: item[1]			)[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__		(							SCREAMING_SNAKE_CASE_					,  unittest.TestCase		):





					"""simple docstring"""






					a_      =						DetrImageProcessor if is_vision_available() else None



					def 			_lowercase	(   self	:    Dict			):
						snake_case__					: Any  =    DetrImageProcessingTester(self			)



					@property
					def 			_lowercase	(   self	:    Dict			):
						return self.image_processor_tester.prepare_image_processor_dict()



					def 			_lowercase	(   self	:    Dict			):
						snake_case__					: str  =    self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(snake_case__				,						"image_mean"			)			)
						self.assertTrue(hasattr(snake_case__				,						"image_std"			)			)
						self.assertTrue(hasattr(snake_case__				,						"do_normalize"			)			)
						self.assertTrue(hasattr(snake_case__				,						"do_rescale"			)			)
						self.assertTrue(hasattr(snake_case__				,						"rescale_factor"			)			)
						self.assertTrue(hasattr(snake_case__				,						"do_resize"			)			)
						self.assertTrue(hasattr(snake_case__				,						"size"			)			)
						self.assertTrue(hasattr(snake_case__				,						"do_pad"			)			)



					def 			_lowercase	(   self	:    List[str]			):
						snake_case__					: Optional[int]  =    self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size				,						{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}			)
						self.assertEqual(image_processor.do_pad				,						snake_case__			)

						snake_case__					: List[str]  =    self.image_processing_class.from_dict(
						    self.image_processor_dict				,						size=4_2				,						max_size=8_4				,						pad_and_return_pixel_mask=snake_case__			)
						self.assertEqual(image_processor.size				,						{"shortest_edge": 4_2, "longest_edge": 8_4}			)
						self.assertEqual(image_processor.do_pad				,						snake_case__			)



					def 			_lowercase	(   self	:    int			):
						pass



					def 			_lowercase	(   self	:    Union[str, Any]			):
						# Initialize image_processing
						snake_case__					: Tuple  =    self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						snake_case__					: Union[str, Any]  =    prepare_image_inputs(self.image_processor_tester				,						equal_resolution=snake_case__			)
						for image in image_inputs:
							self.assertIsInstance(snake_case__				,						Image.Image			)

						# Test not batched input
						snake_case__					: str  =    image_processing(image_inputs[0]				,						return_tensors="pt"			).pixel_values

						snake_case__,		snake_case__					: int  =    self.image_processor_tester.get_expected_values(snake_case__			)

						self.assertEqual(
						    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

						# Test batched
						snake_case__,		snake_case__					: Any  =    self.image_processor_tester.get_expected_values(snake_case__				,						batched=snake_case__			)

						snake_case__					: Any  =    image_processing(snake_case__				,						return_tensors="pt"			).pixel_values
						self.assertEqual(
						    encoded_images.shape				,						(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,						)



					def 			_lowercase	(   self	:    List[str]			):
						# Initialize image_processing
						snake_case__					: List[Any]  =    self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						snake_case__					: int  =    prepare_image_inputs(self.image_processor_tester				,						equal_resolution=snake_case__				,						numpify=snake_case__			)
						for image in image_inputs:
							self.assertIsInstance(snake_case__				,						np.ndarray			)

						# Test not batched input
						snake_case__					: int  =    image_processing(image_inputs[0]				,						return_tensors="pt"			).pixel_values

						snake_case__,		snake_case__					: Tuple  =    self.image_processor_tester.get_expected_values(snake_case__			)

						self.assertEqual(
						    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

						# Test batched
						snake_case__					: Optional[Any]  =    image_processing(snake_case__				,						return_tensors="pt"			).pixel_values

						snake_case__,		snake_case__					: Optional[Any]  =    self.image_processor_tester.get_expected_values(snake_case__				,						batched=snake_case__			)

						self.assertEqual(
						    encoded_images.shape				,						(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,						)



					def 			_lowercase	(   self	:    Dict			):
						# Initialize image_processing
						snake_case__					: str  =    self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						snake_case__					: Tuple  =    prepare_image_inputs(self.image_processor_tester				,						equal_resolution=snake_case__				,						torchify=snake_case__			)
						for image in image_inputs:
							self.assertIsInstance(snake_case__				,						torch.Tensor			)

						# Test not batched input
						snake_case__					: List[str]  =    image_processing(image_inputs[0]				,						return_tensors="pt"			).pixel_values

						snake_case__,		snake_case__					: List[Any]  =    self.image_processor_tester.get_expected_values(snake_case__			)

						self.assertEqual(
						    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

						# Test batched
						snake_case__					: str  =    image_processing(snake_case__				,						return_tensors="pt"			).pixel_values

						snake_case__,		snake_case__					: Union[str, Any]  =    self.image_processor_tester.get_expected_values(snake_case__				,						batched=snake_case__			)

						self.assertEqual(
						    encoded_images.shape				,						(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,						)



					@slow
					def 			_lowercase	(   self	:    List[Any]			):
						# prepare image and target
						snake_case__					: int  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,						"r"			) as f:
							snake_case__					: str  =    json.loads(f.read()			)

						snake_case__					: Any  =    {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__					: int  =    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"			)
						snake_case__					: List[Any]  =    image_processing(images=snake_case__				,						annotations=snake_case__				,						return_tensors="pt"			)

						# verify pixel values
						snake_case__					: Any  =    torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
						self.assertEqual(encoding["pixel_values"].shape				,						snake_case__			)

						snake_case__					: Optional[Any]  =    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,						snake_case__				,						atol=1e-4			)			)

						# verify area
						snake_case__					: Optional[int]  =    torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,						snake_case__			)			)
						# verify boxes
						snake_case__					: int  =    torch.Size([6, 4]			)
						self.assertEqual(encoding["labels"][0]["boxes"].shape				,						snake_case__			)
						snake_case__					: Optional[Any]  =    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,						snake_case__				,						atol=1e-3			)			)
						# verify image_id
						snake_case__					: List[str]  =    torch.tensor([3_9_7_6_9]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,						snake_case__			)			)
						# verify is_crowd
						snake_case__					: Tuple  =    torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,						snake_case__			)			)
						# verify class_labels
						snake_case__					: Union[str, Any]  =    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,						snake_case__			)			)
						# verify orig_size
						snake_case__					: Union[str, Any]  =    torch.tensor([4_8_0, 6_4_0]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,						snake_case__			)			)
						# verify size
						snake_case__					: Optional[Any]  =    torch.tensor([8_0_0, 1_0_6_6]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,						snake_case__			)			)






					@slow
					def 			_lowercase	(   self	:    Any			):
						# prepare image, target and masks_path
						snake_case__					: Any  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,						"r"			) as f:
							snake_case__					: str  =    json.loads(f.read()			)

						snake_case__					: Union[str, Any]  =    {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__					: Optional[int]  =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"			)

						# encode them
						snake_case__					: List[str]  =    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"			)
						snake_case__					: Any  =    image_processing(images=snake_case__				,						annotations=snake_case__				,						masks_path=snake_case__				,						return_tensors="pt"			)

						# verify pixel values
						snake_case__					: Union[str, Any]  =    torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
						self.assertEqual(encoding["pixel_values"].shape				,						snake_case__			)

						snake_case__					: List[str]  =    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,						snake_case__				,						atol=1e-4			)			)

						# verify area
						snake_case__					: Tuple  =    torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,						snake_case__			)			)
						# verify boxes
						snake_case__					: Tuple  =    torch.Size([6, 4]			)
						self.assertEqual(encoding["labels"][0]["boxes"].shape				,						snake_case__			)
						snake_case__					: Optional[int]  =    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,						snake_case__				,						atol=1e-3			)			)
						# verify image_id
						snake_case__					: Optional[int]  =    torch.tensor([3_9_7_6_9]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,						snake_case__			)			)
						# verify is_crowd
						snake_case__					: Dict  =    torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,						snake_case__			)			)
						# verify class_labels
						snake_case__					: Optional[int]  =    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,						snake_case__			)			)
						# verify masks
						snake_case__					: Optional[int]  =    8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,						snake_case__			)
						# verify orig_size
						snake_case__					: List[Any]  =    torch.tensor([4_8_0, 6_4_0]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,						snake_case__			)			)
						# verify size
						snake_case__					: str  =    torch.tensor([8_0_0, 1_0_6_6]			)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,						snake_case__			)			)




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     Tuple=0.0     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     str = "geglu"     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = True     ,  snake_case__     :     str = "layer_norm"     ,  snake_case__     :     bool = False     ,  )      ->	Dict:
				super().__init__()
				_lowerCamelCase				   =  only_cross_attention

				_lowerCamelCase				   =  (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
				_lowerCamelCase				   =  (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

				if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
						raise ValueError(
						    f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
						    f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""		)

				# Define 3 blocks. Each block has its own normalization layer.
				# 1. Self-Attn
				if self.use_ada_layer_norm:
						_lowerCamelCase				   =  AdaLayerNorm(snake_case__     ,  snake_case__		)
				elif self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  AdaLayerNormZero(snake_case__     ,  snake_case__		)
				else:
						_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
				_lowerCamelCase				   =  Attention(
				    query_dim=snake_case__     ,  heads=snake_case__     ,  dim_head=snake_case__     ,  dropout=snake_case__     ,  bias=snake_case__     ,  cross_attention_dim=cross_attention_dim if only_cross_attention else None     ,  upcast_attention=snake_case__     ,  )

				# 2. Cross-Attn
				if cross_attention_dim is not None or double_self_attention:
						# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
						# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
						# the second cross attention block.
						_lowerCamelCase				   =  (
						    AdaLayerNorm(snake_case__     ,  snake_case__		)
						    if self.use_ada_layer_norm
						    else nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
						)
						_lowerCamelCase				   =  Attention(
						    query_dim=snake_case__     ,  cross_attention_dim=cross_attention_dim if not double_self_attention else None     ,  heads=snake_case__     ,  dim_head=snake_case__     ,  dropout=snake_case__     ,  bias=snake_case__     ,  upcast_attention=snake_case__     ,  )  # is self-attn if encoder_hidden_states is none
				else:
						_lowerCamelCase				   =  None
						_lowerCamelCase				   =  None

				# 3. Feed-forward
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
				_lowerCamelCase				   =  FeedForward(snake_case__     ,  dropout=snake_case__     ,  activation_fn=snake_case__     ,  final_dropout=snake_case__		)

				# let chunk size default to None
				_lowerCamelCase				   =  None
				_lowerCamelCase				   =  0
		def   _snake_case       (       self     :     str     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     int		)      ->	Optional[Any]:
				# Sets chunk feed-forward
				_lowerCamelCase				   =  chunk_size
				_lowerCamelCase				   =  dim



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     torch.FloatTensor     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.LongTensor] = None     ,  snake_case__     :     Dict[str, Any] = None     ,  snake_case__     :     Optional[torch.LongTensor] = None     ,  )      ->	List[Any]:
				# Notice that normalization is always applied before the real computation in the following blocks.
				# 1. Self-Attention
				if self.use_ada_layer_norm:
						_lowerCamelCase				   =  self.norma(snake_case__     ,  snake_case__		)
				elif self.use_ada_layer_norm_zero:
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  self.norma(
						    snake_case__     ,  snake_case__     ,  snake_case__     ,  hidden_dtype=hidden_states.dtype		)
				else:
						_lowerCamelCase				   =  self.norma(snake_case__		)

				_lowerCamelCase				   =  cross_attention_kwargs if cross_attention_kwargs is not None else {}

				_lowerCamelCase				   =  self.attna(
				    snake_case__     ,  encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None     ,  attention_mask=snake_case__     ,  **snake_case__     ,  )
				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  gate_msa.unsqueeze(1		) * attn_output
				_lowerCamelCase				   =  attn_output + hidden_states

				# 2. Cross-Attention
				if self.attna is not None:
						_lowerCamelCase				   =  (
						    self.norma(snake_case__     ,  snake_case__		) if self.use_ada_layer_norm else self.norma(snake_case__		)
						)

						_lowerCamelCase				   =  self.attna(
						    snake_case__     ,  encoder_hidden_states=snake_case__     ,  attention_mask=snake_case__     ,  **snake_case__     ,  )
						_lowerCamelCase				   =  attn_output + hidden_states

				# 3. Feed-forward
				_lowerCamelCase				   =  self.norma(snake_case__		)

				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

				if self._chunk_size is not None:
						# "feed_forward_chunk_size" can be used to save memory
						if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
								raise ValueError(
								    f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""		)

						_lowerCamelCase				   =  norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
						_lowerCamelCase				   =  torch.cat(
						    [self.ff(snake_case__		) for hid_slice in norm_hidden_states.chunk(snake_case__     ,  dim=self._chunk_dim		)]     ,  dim=self._chunk_dim     ,  )
				else:
						_lowerCamelCase				   =  self.ff(snake_case__		)

				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  gate_mlp.unsqueeze(1		) * ff_output

				_lowerCamelCase				   =  ff_output + hidden_states

				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     int = 4     ,  snake_case__     :     float = 0.0     ,  snake_case__     :     str = "geglu"     ,  snake_case__     :     bool = False     ,  )      ->	Optional[int]:
				super().__init__()
				_lowerCamelCase				   =  int(dim * mult		)
				_lowerCamelCase				   =  dim_out if dim_out is not None else dim

				if activation_fn == "gelu":
						_lowerCamelCase				   =  GELU(snake_case__     ,  snake_case__		)
				if activation_fn == "gelu-approximate":
						_lowerCamelCase				   =  GELU(snake_case__     ,  snake_case__     ,  approximate='tanh'		)
				elif activation_fn == "geglu":
						_lowerCamelCase				   =  GEGLU(snake_case__     ,  snake_case__		)
				elif activation_fn == "geglu-approximate":
						_lowerCamelCase				   =  ApproximateGELU(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  nn.ModuleList([]		)
				# project in
				self.net.append(snake_case__		)
				# project dropout
				self.net.append(nn.Dropout(snake_case__		)		)
				# project out
				self.net.append(nn.Linear(snake_case__     ,  snake_case__		)		)
				# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
				if final_dropout:
						self.net.append(nn.Dropout(snake_case__		)		)



		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     List[Any]		)      ->	Optional[int]:
				for module in self.net:
						_lowerCamelCase				   =  module(snake_case__		)
				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     str     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     str = "none"		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  snake_case__		)
				_lowerCamelCase				   =  approximate
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     List[Any]		)      ->	int:
				if gate.device.type != "mps":
						return F.gelu(snake_case__     ,  approximate=self.approximate		)
				# mps: gelu is not implemented for float16
				return F.gelu(gate.to(dtype=torch.floataa		)     ,  approximate=self.approximate		).to(dtype=gate.dtype		)



		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     Union[str, Any]		)      ->	str:
				_lowerCamelCase				   =  self.proj(snake_case__		)
				_lowerCamelCase				   =  self.gelu(snake_case__		)
				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	List[Any]:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  dim_out * 2		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Optional[int]		)      ->	Optional[Any]:
				if gate.device.type != "mps":
						return F.gelu(snake_case__		)
				# mps: gelu is not implemented for float16
				return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     Optional[Any]		)      ->	Optional[int]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.proj(snake_case__		).chunk(2     ,  dim=-1		)
				return hidden_states * self.gelu(snake_case__		)





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  snake_case__		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     List[Any]		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.proj(snake_case__		)
				return x * torch.sigmoid(1.702 * x		)





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Optional[Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     Any		)      ->	Dict:
				super().__init__()
				_lowerCamelCase				   =  nn.Embedding(snake_case__     ,  snake_case__		)
				_lowerCamelCase				   =  nn.SiLU()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  embedding_dim * 2		)
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)



		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     str     ,  snake_case__     :     Dict		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.linear(self.silu(self.emb(snake_case__		)		)		)
				_lowerCamelCase   ,							_lowerCamelCase				   =  torch.chunk(snake_case__     ,  2		)
				_lowerCamelCase				   =  self.norm(snake_case__		) * (1 + scale) + shift
				return x





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Any     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Union[str, Any]		)      ->	Dict:
				super().__init__()

				_lowerCamelCase				   =  CombinedTimestepLabelEmbeddings(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  nn.SiLU()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  6 * embedding_dim     ,  bias=snake_case__		)
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__     ,  eps=1e-6		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Tuple     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     List[Any]=None		)      ->	int:
				_lowerCamelCase				   =  self.linear(self.silu(self.emb(snake_case__     ,  snake_case__     ,  hidden_dtype=snake_case__		)		)		)
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  emb.chunk(6     ,  dim=1		)
				_lowerCamelCase				   =  self.norm(snake_case__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
				return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Tuple     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     Optional[str] = None     ,  snake_case__     :     float = 1e-5		)      ->	int:
				super().__init__()
				_lowerCamelCase				   =  num_groups
				_lowerCamelCase				   =  eps

				if act_fn is None:
						_lowerCamelCase				   =  None
				else:
						_lowerCamelCase				   =  get_activation(snake_case__		)

				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  out_dim * 2		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[Any]		)      ->	Union[str, Any]:
				if self.act:
						_lowerCamelCase				   =  self.act(snake_case__		)
				_lowerCamelCase				   =  self.linear(snake_case__		)
				_lowerCamelCase				   =  emb[:, :, None, None]
				_lowerCamelCase   ,							_lowerCamelCase				   =  emb.chunk(2     ,  dim=1		)

				_lowerCamelCase				   =  F.group_norm(snake_case__     ,  self.num_groups     ,  eps=self.eps		)
				_lowerCamelCase				   =  x * (1 + scale) + shift
				return x